Detection of Non-Coding RNA's with Optimized Support Vector Machines

Arslan A., ŞEN B.

23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Turkey, 16 - 19 May 2015, pp.1668-1671 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2015.7130172
  • City: Malatya
  • Country: Turkey
  • Page Numbers: pp.1668-1671


Non-coding RNAs (ncRNAs) are started to work by a lot of scientists in recent years. ncRNAs are playing important roles in the cell and many of them are waiting to be discovered. The Support Vector Machine (SVM) is quite widely used machine learning algorithm in classification problems. Classification process is being difficult when number of problem instances is increased. The classication processes that will take a lot of time when executed on CPU, can be run and optimized in parallel by using multi core platform which is provided by GPU. In this study, detection of ncRNA's was studied by using a large scaled genomic sequence dataset. NVIDIA CUDA parallel programming technology is utilized to be able to accelerate training and test processes that were implemented by SVM. At the end of this study, detection of ncRNA's by using GPU is successfully implemented in shorter time than CPU and with the same success.